The Attitude Fusion Algorithm On Multi-MEMS Inertial Sensors

With the development and innovation of microelectronics technology, MEMS inertial sensors, as the key devices of data acquisition, have occupied a wider applications field. MEMS inertial sensors have many advantages, such as light weight, small size, inexpensive cost, moderate accuracy. We can find a variety of inertial sensors combined measurement system from the earliest industrial and military aerospace to the current consumer electronics market. Through continuous development in recent decades, MEMS technology has been gradually maturing. However it still has an improvement area in the specific applications, especially for the object attitude error in the measurement process and real-time output. Therefore it still has a need for further exploration and research on the use of attitude measurement information from MEMS inertial sensors.The key technologies of sensor data processing and attitude fusion algorithm was studied systematically. With inertial sensors as the tool, this work established attitude fusion algorithms model and simulation studies.And with the basic principles of theoretical research, MEMS inertial sensor fusion system was based on the current development and research. The common several attitude algorithms features were analyzed and comparised. Under the inertial sensors measurement in attitude establishing, this work set up the corresponding data filter model. With the contrast of sensor data filtering and origin sensor data, filter model proposed by this work had an obvious denoising effect. Then, the work used the accelerometer and gyroscope for attitude output theoretical analysis and established a model of attitude fusion algorithms. The result showed that the model above had a higher accuracy and more reliable output compared to traditional gesture recognition algorithm. To get a further improvement and real-time accuracy of this article attitude fusion algorithms, this work introduced complementary filter and extended Kalman filter. According to the attitude fusion algorithm model of this work, the scheme respectively established a complementary filter algorithm model and extended Kalman filter algorithm model. When the attitude fusion started a processing, accelerometer and gyroscope got a real-time output which improved the output attitude angle precision based on inertial sensor fusion algorithm. It improved attitude fusion system in the real-time attitude updating by using quaternion output of MEMS inertial sensors. This work designed two different filters based on quaternion attitude algorithm model. The methods proposed by this work met the demand of consumer electronics products in the attitude information filter and the Extended Kalman Filter base on quaternion attitude fusion algorithms had a greater accuracy by vetification.